Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,655
2	New Jersey	19,731
3	Rhode Island	16,038
4	Massachusetts	15,955
5	District of Columbia	14,852
6	Louisiana	14,053
7	Arizona	13,481
8	Connecticut	13,103
9	Delaware	12,454
10	Illinois	11,737
11	Maryland	11,571
12	Mississippi	10,382
13	Nebraska	10,307
14	Iowa	10,019
15	Florida	9,316
16	Alabama	8,964
17	South Carolina	8,710
18	Georgia	8,428
19	South Dakota	7,983
20	Arkansas	7,891
21	Utah	7,799
22	Virginia	7,702
23	Pennsylvania	7,374
24	Tennessee	7,374
25	Michigan	7,312
26	Nevada	7,310
27	Indiana	7,308
28	North Carolina	6,969
29	Texas	6,902
30	California	6,873
31	Minnesota	6,768
32	New Mexico	6,321
33	Wisconsin	5,968
34	Colorado	5,921
35	Kansas	5,591
36	North Dakota	5,019
37	Washington	4,920
38	Ohio	4,889
39	Idaho	4,364
40	New Hampshire	4,336
41	Oklahoma	4,025
42	Missouri	3,964
43	Kentucky	3,740
44	Wyoming	2,823
45	Maine	2,540
46	Puerto Rico	2,478
47	Oregon	2,427
48	Vermont	2,001
49	Alaska	1,874
50	West Virginia	1,860
51	Montana	1,134
52	Hawaii	709

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	486
2	Florida	481
3	South Carolina	333
4	Nevada	286
5	Louisiana	262
6	Alabama	261
7	Mississippi	238
8	Georgia	220
9	Idaho	207
10	Texas	205
11	Tennessee	204
12	California	197
13	Arkansas	192
14	Utah	160
15	North Carolina	145
16	Iowa	142
17	Delaware	135
18	Kansas	121
19	New Mexico	117
20	Oklahoma	117
21	Wisconsin	107
22	Ohio	85
23	Indiana	81
24	Nebraska	79
25	Virginia	78
26	Oregon	74
27	North Dakota	69
28	Missouri	67
29	Washington	67
30	Alaska	66
31	South Dakota	64
32	Illinois	61
33	Minnesota	54
34	West Virginia	52
35	Maryland	51
36	Wyoming	48
37	Colorado	44
38	Pennsylvania	44
39	District of Columbia	43
40	Montana	40
41	Michigan	39
42	New Jersey	39
43	New York	37
44	Puerto Rico	32
45	Massachusetts	30
46	Kentucky	25
47	Maine	21
48	New Hampshire	18
49	Hawaii	17
50	Rhode Island	15
51	Vermont	11
52	Connecticut	6

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,712
2	New York	1,639
3	Connecticut	1,215
4	Massachusetts	1,187
5	Rhode Island	906
6	District of Columbia	792
7	Louisiana	707
8	Michigan	623
9	Illinois	571
10	Maryland	536
11	Pennsylvania	530
12	Delaware	525
13	Indiana	400
14	Mississippi	373
15	Colorado	295
16	New Hampshire	280
17	Minnesota	267
18	Georgia	264
19	Arizona	251
20	Ohio	249
21	New Mexico	244
22	Iowa	228
23	Virginia	217
24	Alabama	205
25	Washington	178
26	Nevada	174
27	Florida	173
28	Missouri	173
29	California	161
30	South Carolina	159
31	Nebraska	147
32	Wisconsin	138
33	Kentucky	136
34	North Carolina	134
35	North Dakota	116
36	South Dakota	109
37	Oklahoma	100
38	Kansas	98
39	Arkansas	95
40	Tennessee	93
41	Texas	91
42	Vermont	89
43	Maine	81
44	Utah	57
45	West Virginia	53
46	Idaho	52
47	Oregon	51
48	Puerto Rico	48
49	Wyoming	34
50	Montana	21
51	Alaska	19
52	Hawaii	13

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	3
2	Arizona	2
3	District of Columbia	2
4	Louisiana	2
5	Massachusetts	2
6	Mississippi	2
7	South Carolina	2
8	Alabama	1
9	Florida	1
10	Illinois	1
11	Indiana	1
12	Maryland	1
13	Nevada	1
14	New Hampshire	1
15	New Mexico	1
16	New York	1
17	Pennsylvania	1
18	Tennessee	1
19	Texas	1
20	Virginia	1
21	Alaska	0
22	Arkansas	0
23	California	0
24	Colorado	0
25	Connecticut	0
26	Delaware	0
27	Georgia	0
28	Hawaii	0
29	Idaho	0
30	Iowa	0
31	Kansas	0
32	Kentucky	0
33	Maine	0
34	Michigan	0
35	Minnesota	0
36	Missouri	0
37	Montana	0
38	Nebraska	0
39	North Carolina	0
40	North Dakota	0
41	Ohio	0
42	Oklahoma	0
43	Oregon	0
44	Puerto Rico	0
45	Rhode Island	0
46	South Dakota	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	132,754	1	99
Lake	Tennessee	98,632	2	99
Lee	Arkansas	90,663	3	99
Dakota	Nebraska	89,334	4	99
Buena Vista	Iowa	87,258	5	99
Davidson	Tennessee	16,955	163	94
Richland	South Carolina	9,926	448	85
Orange	California	6,061	841	73
York	South Carolina	5,278	980	68
Pierce	Washington	3,480	1408	55

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,165	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	209	681	78
Davidson	Tennessee	176	752	76
Pierce	Washington	118	982	68
Orange	California	114	1007	67
York	South Carolina	43	1576	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.